{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import CLIP.clip as clip\n",
    "clip.available_models()\n",
    "import torch\n",
    "import numpy as np\n",
    "torch.__version__\n",
    "import pickle\n",
    "import json\n",
    "from utils import custom_collate, CustomisedDLE, DataFactory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train_dataset = DataFactory(name='vcoco', partition='trainval', data_root='vcoco')\n",
    "test_dataset = DataFactory(name='vcoco', partition='test', data_root='vcoco')\n",
    "OBJECTS = [\n",
    "    'person', \n",
    "    'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', \n",
    "    'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', \n",
    "    'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', \n",
    "    'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', \n",
    "    'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', \n",
    "    'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', \n",
    "    'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven',\n",
    "    'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear','hair drier', 'toothbrush']\n",
    "vcoco_verb_name_dict = {0:'hold obj', 1:'stand', 2:'sit instr', 3:'ride instr', 4:'walk', 5:'look obj', 6:'hit instr', 7:'hit obj',\n",
    "                        8:'eat obj', 9:'eat instr', 10:'jump instr', 11:'lay instr', 12:'talk_on_phone instr', 13:'carry obj',\n",
    "                        14:'throw obj', 15:'catch obj', 16:'cut instr', 17:'cut obj', 18:'run', 19:'work_on_computer instr',\n",
    "                        20:'ski instr', 21:'surf instr', 22:'skateboard instr', 23:'smile', 24:'drink instr', 25:'kick obj',\n",
    "                        26:'point instr', 27:'read obj', 28:'snowboard instr'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['annotations', 'classes', 'objects', 'images', 'action_to_object'])\n",
      "测试图片数量4946\n",
      "dict_keys(['file_name', 'boxes_h', 'boxes_o', 'actions', 'objects'])\n"
     ]
    }
   ],
   "source": [
    "anno_file = '/root/whzh/codes/HOI/upt/vcoco/instances_vcoco_test.json'\n",
    "with open(anno_file, 'r') as f:\n",
    "    anno = json.load(f)\n",
    "print(anno.keys())\n",
    "print(\"测试图片数量\" +str(len(anno['annotations'])))\n",
    "print(anno['annotations'][0].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2346881\n"
     ]
    }
   ],
   "source": [
    "detections_file = '/root/whzh/codes/HOI/upt/logs/vcoco/debug/cache.pkl'\n",
    "with open(detections_file, 'rb') as f:\n",
    "    dets = pickle.load(f)\n",
    "print(len(dets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('person',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('bicycle',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'sit instr',\n",
       "   'ride instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('car',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'ride instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('motorcycle',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'sit instr',\n",
       "   'ride instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('airplane',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'ride instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('bus',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'ride instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('train',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'ride instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('truck',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'ride instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('boat',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'ride instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('traffic light',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('fire hydrant',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('stop sign',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('parking meter',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('bench',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'sit instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'lay instr',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('bird',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('cat',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('dog',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('horse',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'sit instr',\n",
       "   'ride instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('sheep',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('cow',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('elephant',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'sit instr',\n",
       "   'ride instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('bear',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('zebra',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('giraffe',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('backpack',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'sit instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('umbrella',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('handbag',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'sit instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('tie',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('suitcase',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'sit instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('frisbee',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'throw obj',\n",
       "   'catch obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('skis',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'jump instr',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'ski instr',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('snowboard',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'jump instr',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr',\n",
       "   'snowboard instr']),\n",
       " ('sports ball',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'hit obj',\n",
       "   'carry obj',\n",
       "   'throw obj',\n",
       "   'catch obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'kick obj',\n",
       "   'point instr']),\n",
       " ('kite',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('baseball bat',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'hit instr',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('baseball glove',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('skateboard',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'jump instr',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'skateboard instr',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('surfboard',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'jump instr',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'surf instr',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('tennis racket',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'hit instr',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('bottle',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'drink instr',\n",
       "   'point instr']),\n",
       " ('wine glass',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'drink instr',\n",
       "   'point instr']),\n",
       " ('cup',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'drink instr',\n",
       "   'point instr']),\n",
       " ('fork',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'eat instr',\n",
       "   'carry obj',\n",
       "   'cut instr',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('knife',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'eat instr',\n",
       "   'carry obj',\n",
       "   'cut instr',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('spoon',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'eat instr',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('bowl',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'drink instr',\n",
       "   'point instr']),\n",
       " ('banana',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'eat obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('apple',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'eat obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('sandwich',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'eat obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('orange',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'eat obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('broccoli',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'eat obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('carrot',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'eat obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('hot dog',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'eat obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('pizza',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'eat obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('donut',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'eat obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('cake',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'eat obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('chair',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'sit instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'lay instr',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('couch',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'sit instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'lay instr',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('potted plant',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('bed',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'sit instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'lay instr',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('dining table',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'sit instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'lay instr',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('toilet',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'sit instr',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'lay instr',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('tv',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('laptop',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'work_on_computer instr',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('mouse',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('remote',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('keyboard',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('cell phone',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'talk_on_phone instr',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('microwave',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('oven',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('toaster',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('sink',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('refrigerator',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('book',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr',\n",
       "   'read obj']),\n",
       " ('clock',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('vase',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('scissors',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut instr',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('teddy bear',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('hair drier',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr']),\n",
       " ('toothbrush',\n",
       "  ['hold obj',\n",
       "   'stand',\n",
       "   'walk',\n",
       "   'look obj',\n",
       "   'carry obj',\n",
       "   'cut obj',\n",
       "   'run',\n",
       "   'smile',\n",
       "   'point instr'])]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[ (OBJECTS[i], list(np.array(list(vcoco_verb_name_dict.values()))[v])) for i, v in enumerate(list(train_dataset.dataset.object_to_action.values()))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('hold obj', 4001), ('stand', 4598), ('sit instr', 1989), ('ride instr', 488), ('walk', 656), ('look obj', 3825), ('hit instr', 367), ('hit obj', 367), ('eat obj', 677), ('eat instr', 677), ('jump instr', 700), ('lay instr', 471), ('talk_on_phone instr', 354), ('carry obj', 498), ('throw obj', 300), ('catch obj', 313), ('cut instr', 300), ('cut obj', 300), ('run', 622), ('work_on_computer instr', 458), ('ski instr', 500), ('surf instr', 498), ('skateboard instr', 489), ('smile', 1545), ('drink instr', 133), ('kick obj', 142), ('point instr', 38), ('read obj', 116), ('snowboard instr', 388)]\n",
      "==============================================\n",
      "[('hold obj', 3608), ('stand', 4118), ('sit instr', 1916), ('ride instr', 556), ('walk', 597), ('look obj', 3347), ('hit instr', 349), ('hit obj', 349), ('eat obj', 521), ('eat instr', 521), ('jump instr', 635), ('lay instr', 387), ('talk_on_phone instr', 285), ('carry obj', 472), ('throw obj', 244), ('catch obj', 246), ('cut instr', 269), ('cut obj', 269), ('run', 687), ('work_on_computer instr', 410), ('ski instr', 424), ('surf instr', 486), ('skateboard instr', 417), ('smile', 1415), ('drink instr', 82), ('kick obj', 180), ('point instr', 31), ('read obj', 111), ('snowboard instr', 277)]\n"
     ]
    }
   ],
   "source": [
    "print([(vcoco_verb_name_dict[i], v) for i, v in enumerate(train_dataset.dataset._num_instances)])\n",
    "print(\"==============================================\")\n",
    "print([(vcoco_verb_name_dict[i], v) for i, v in enumerate(test_dataset.dataset._num_instances)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(test_dataset.dataset._image_ids))\n",
    "print(dets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('hold obj',\n",
       "  ['book',\n",
       "   'cell phone',\n",
       "   'tennis racket',\n",
       "   'knife',\n",
       "   'skis',\n",
       "   'donut',\n",
       "   'hot dog',\n",
       "   'sandwich',\n",
       "   'scissors',\n",
       "   'person',\n",
       "   'baseball bat',\n",
       "   'surfboard',\n",
       "   'frisbee',\n",
       "   'suitcase',\n",
       "   'remote',\n",
       "   'broccoli',\n",
       "   'skateboard',\n",
       "   'handbag',\n",
       "   'motorcycle',\n",
       "   'laptop',\n",
       "   'teddy bear',\n",
       "   'backpack',\n",
       "   'toothbrush',\n",
       "   'carrot',\n",
       "   'cup',\n",
       "   'apple',\n",
       "   'snowboard',\n",
       "   'fork',\n",
       "   'pizza',\n",
       "   'baseball glove',\n",
       "   'bottle',\n",
       "   'sports ball',\n",
       "   'horse',\n",
       "   'mouse',\n",
       "   'truck',\n",
       "   'cake',\n",
       "   'wine glass',\n",
       "   'sheep',\n",
       "   'cow',\n",
       "   'dog',\n",
       "   'bicycle',\n",
       "   'kite',\n",
       "   'spoon',\n",
       "   'umbrella',\n",
       "   'bench',\n",
       "   'banana',\n",
       "   'bowl',\n",
       "   'tie',\n",
       "   'chair',\n",
       "   'elephant',\n",
       "   'dining table',\n",
       "   'hair drier',\n",
       "   'boat',\n",
       "   'keyboard',\n",
       "   'bed',\n",
       "   'cat',\n",
       "   'refrigerator',\n",
       "   'orange',\n",
       "   'bird']),\n",
       " ('sit instr',\n",
       "  ['bench',\n",
       "   'chair',\n",
       "   'couch',\n",
       "   'motorcycle',\n",
       "   'horse',\n",
       "   'toilet',\n",
       "   'bed',\n",
       "   'bicycle',\n",
       "   'suitcase',\n",
       "   'elephant',\n",
       "   'dining table',\n",
       "   'backpack',\n",
       "   'handbag']),\n",
       " ('ride instr',\n",
       "  ['motorcycle',\n",
       "   'horse',\n",
       "   'truck',\n",
       "   'bicycle',\n",
       "   'boat',\n",
       "   'elephant',\n",
       "   'train',\n",
       "   'car',\n",
       "   'bus']),\n",
       " ('look obj',\n",
       "  ['book',\n",
       "   'frisbee',\n",
       "   'skateboard',\n",
       "   'snowboard',\n",
       "   'cake',\n",
       "   'laptop',\n",
       "   'sports ball',\n",
       "   'skis',\n",
       "   'person',\n",
       "   'sandwich',\n",
       "   'scissors',\n",
       "   'pizza',\n",
       "   'surfboard',\n",
       "   'knife',\n",
       "   'tennis racket',\n",
       "   'traffic light',\n",
       "   'suitcase',\n",
       "   'dining table',\n",
       "   'dog',\n",
       "   'train',\n",
       "   'bus',\n",
       "   'sheep',\n",
       "   'cow',\n",
       "   'horse',\n",
       "   'tv',\n",
       "   'donut',\n",
       "   'cell phone',\n",
       "   'cup',\n",
       "   'baseball bat',\n",
       "   'spoon',\n",
       "   'car',\n",
       "   'kite',\n",
       "   'banana',\n",
       "   'bowl',\n",
       "   'remote',\n",
       "   'bird',\n",
       "   'tie',\n",
       "   'baseball glove',\n",
       "   'hot dog',\n",
       "   'handbag',\n",
       "   'chair',\n",
       "   'wine glass',\n",
       "   'umbrella',\n",
       "   'airplane',\n",
       "   'apple',\n",
       "   'bottle',\n",
       "   'truck',\n",
       "   'mouse',\n",
       "   'cat',\n",
       "   'fork',\n",
       "   'motorcycle',\n",
       "   'keyboard',\n",
       "   'boat',\n",
       "   'elephant',\n",
       "   'potted plant',\n",
       "   'bicycle',\n",
       "   'clock',\n",
       "   'backpack',\n",
       "   'bench',\n",
       "   'refrigerator',\n",
       "   'fire hydrant',\n",
       "   'broccoli',\n",
       "   'giraffe',\n",
       "   'toilet']),\n",
       " ('hit instr', ['tennis racket', 'baseball bat']),\n",
       " ('hit obj', ['sports ball']),\n",
       " ('eat obj',\n",
       "  ['cake',\n",
       "   'donut',\n",
       "   'hot dog',\n",
       "   'sandwich',\n",
       "   'broccoli',\n",
       "   'carrot',\n",
       "   'apple',\n",
       "   'pizza',\n",
       "   'orange',\n",
       "   'banana']),\n",
       " ('eat instr', ['fork', 'spoon', 'knife']),\n",
       " ('jump instr', ['skateboard', 'snowboard', 'skis', 'surfboard']),\n",
       " ('lay instr', ['couch', 'bed', 'bench', 'chair', 'toilet', 'dining table']),\n",
       " ('talk_on_phone instr', ['cell phone']),\n",
       " ('carry obj',\n",
       "  ['backpack',\n",
       "   'person',\n",
       "   'surfboard',\n",
       "   'suitcase',\n",
       "   'handbag',\n",
       "   'cell phone',\n",
       "   'skis',\n",
       "   'teddy bear',\n",
       "   'donut',\n",
       "   'baseball bat',\n",
       "   'umbrella',\n",
       "   'kite',\n",
       "   'snowboard',\n",
       "   'dog',\n",
       "   'laptop',\n",
       "   'frisbee',\n",
       "   'banana',\n",
       "   'car',\n",
       "   'bicycle',\n",
       "   'skateboard',\n",
       "   'sheep',\n",
       "   'sports ball',\n",
       "   'tennis racket',\n",
       "   'bottle',\n",
       "   'wine glass',\n",
       "   'cup',\n",
       "   'book',\n",
       "   'clock',\n",
       "   'orange']),\n",
       " ('throw obj', ['frisbee', 'sports ball']),\n",
       " ('catch obj', ['sports ball', 'frisbee']),\n",
       " ('cut instr', ['knife', 'scissors', 'fork']),\n",
       " ('cut obj',\n",
       "  ['cake',\n",
       "   'sandwich',\n",
       "   'pizza',\n",
       "   'apple',\n",
       "   'skateboard',\n",
       "   'person',\n",
       "   'banana',\n",
       "   'hot dog',\n",
       "   'orange',\n",
       "   'book',\n",
       "   'carrot',\n",
       "   'bowl',\n",
       "   'donut',\n",
       "   'laptop',\n",
       "   'broccoli',\n",
       "   'sheep',\n",
       "   'tie']),\n",
       " ('work_on_computer instr', ['laptop']),\n",
       " ('ski instr', ['skis']),\n",
       " ('surf instr', ['surfboard']),\n",
       " ('skateboard instr', ['skateboard']),\n",
       " ('drink instr', ['cup', 'wine glass', 'bottle', 'bowl']),\n",
       " ('kick obj', ['sports ball']),\n",
       " ('read obj', ['book']),\n",
       " ('snowboard instr', ['snowboard'])]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_list = train_dataset.dataset._action_to_object\n",
    "[(vcoco_verb_name_dict[i], list(np.array(OBJECTS)[np.array(v)-1])) for i, v in enumerate(train_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'image_id': 685627, 'person_box': 685627, 'hold_agent': 158045, 'hold_obj': 158045, 'look_agent': 157824, 'look_obj': 157824, 'carry_agent': 130537, 'carry_obj': 130537, 'cut_agent': 83352, 'cut_obj': 80125, 'sit_agent': 27233, 'sit_instr': 27233, 'lay_agent': 13992, 'lay_instr': 13992, 'read_agent': 1305, 'read_obj': 1305, 'talk_on_phone_agent': 4325, 'talk_on_phone_instr': 4325, 'eat_agent': 8665, 'eat_obj': 5292, 'eat_instr': 3372, 'cut_instr': 3226, 'hit_agent': 9502, 'hit_instr': 3278, 'jump_agent': 24921, 'jump_instr': 24921, 'skateboard_agent': 5183, 'skateboard_instr': 5183, 'throw_agent': 10397, 'throw_obj': 10397, 'catch_agent': 10397, 'catch_obj': 10397, 'snowboard_agent': 4241, 'snowboard_instr': 4241, 'ski_agent': 6483, 'ski_instr': 6483, 'work_on_computer_agent': 2876, 'work_on_computer_instr': 2876, 'hit_obj': 6223, 'kick_agent': 6223, 'kick_obj': 6223, 'ride_agent': 6536, 'ride_instr': 6536, 'surf_agent': 9011, 'surf_instr': 9011, 'drink_agent': 4559, 'drink_instr': 4559}\n",
      "==============================================\n",
      "[('hold obj', 2733), ('sit instr', 1089), ('ride instr', 505), ('look obj', 2752), ('hit instr', 315), ('hit obj', 226), ('eat obj', 334), ('eat instr', 100), ('jump instr', 402), ('lay instr', 230), ('talk_on_phone instr', 246), ('carry obj', 433), ('throw obj', 214), ('catch obj', 201), ('cut instr', 230), ('cut obj', 203), ('work_on_computer instr', 375), ('ski instr', 360), ('surf instr', 464), ('skateboard instr', 402), ('drink instr', 78), ('kick obj', 163), ('read obj', 86), ('snowboard instr', 261)]\n"
     ]
    }
   ],
   "source": [
    "act_dic = dict()\n",
    "for det in dets:\n",
    "    for key in det.keys():\n",
    "        if key in act_dic.keys():\n",
    "            act_dic[key] += 1\n",
    "        else:\n",
    "            act_dic[key] = 0\n",
    "print(act_dic)\n",
    "print(\"==============================================\")\n",
    "print([(vcoco_verb_name_dict[i], v) for i, v in enumerate(test_dataset.dataset._num_instances)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3373\n",
      "357\n",
      "357\n",
      "dict_keys(['boxes_h', 'boxes_o', 'actions', 'objects', 'file_name'])\n",
      "89\n",
      "90\n"
     ]
    }
   ],
   "source": [
    "eat_det = np.array(dets)[[('eat_instr' in det.keys()) for det in dets ]]\n",
    "print(len(eat_det))\n",
    "\n",
    "eat_set = set()\n",
    "for det in eat_det:\n",
    "    eat_set.add(det['image_id'])\n",
    "print(len(eat_set))\n",
    "\n",
    "show_list = list()\n",
    "for an in anno['annotations']:\n",
    "    for idx in list(eat_set):\n",
    "        if '0'+str(idx)+'.' in an['file_name']:\n",
    "            show_list.append(an)\n",
    "print(len(show_list))\n",
    "\n",
    "print(show_list[0].keys())\n",
    "print(len(np.array(show_list)[ [(7 in l['actions']) for l in show_list]]))\n",
    "print(len(np.array(anno['annotations'])[ [(7 in l['actions']) for l in anno['annotations']]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['boxes_h', 'boxes_o', 'actions', 'objects', 'file_name'])\n"
     ]
    }
   ],
   "source": [
    "eat_annos  = np.array(anno['annotations'])[[(7 in l['actions']) for l in anno['annotations']]]\n",
    "print(eat_annos[0].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_86962/2010984367.py:1: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  [ np.array(eat_anno[\"boxes_o\"])[[eat_anno[\"actions\"] == np.ones_like(eat_anno[\"actions\"]) * 7]] for eat_anno in eat_annos]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[array([[111.43, 209.19, 152.48, 223.85]]),\n",
       " array([[306.48, 274.24, 441.31, 320.04],\n",
       "        [ 44.99, 335.35,  87.75, 433.53]]),\n",
       " array([[455.62, 106.43, 640.  , 179.85]]),\n",
       " array([[252.52, 203.38, 280.36, 243.49],\n",
       "        [142.7 , 420.73, 230.99, 461.05]]),\n",
       " array([[198.19, 187.6 , 205.06, 332.39]]),\n",
       " array([[211.27, 232.67, 316.03, 271.58]]),\n",
       " array([[166.16, 326.58, 308.04, 452.29]]),\n",
       " array([[370.5 , 465.11, 469.69, 488.66]]),\n",
       " array([[150.88, 272.14, 231.31, 319.35]]),\n",
       " array([[137.92, 199.37, 195.15, 209.83],\n",
       "        [542.17, 313.84, 627.77, 347.22]]),\n",
       " array([[111.71, 368.3 , 131.28, 488.88]]),\n",
       " array([[275.66, 271.72, 379.47, 386.55]]),\n",
       " array([[301.4 , 399.57, 436.52, 436.8 ]]),\n",
       " array([[296.84, 134.16, 360.28, 223.77]]),\n",
       " array([[370.39, 222.86, 389.34, 240.09],\n",
       "        [237.32, 254.09, 272.41, 267.6 ]]),\n",
       " array([[192.96, 236.31, 214.16, 243.63]]),\n",
       " array([[217.35, 344.99, 242.36, 398.71]]),\n",
       " array([[211.9 ,  81.47, 261.27, 124.26]]),\n",
       " array([[ 35.91,  12.03, 136.72,  51.7 ]]),\n",
       " array([[172.68, 248.57, 248.48, 366.58]]),\n",
       " array([[447.02, 229.13, 487.28, 257.65]]),\n",
       " array([[161.51, 264.28, 225.99, 283.92]]),\n",
       " array([[225.72, 120.26, 316.15, 201.25]]),\n",
       " array([[251.81, 266.69, 302.07, 282.09]]),\n",
       " array([[245.46, 245.56, 270.11, 331.87]]),\n",
       " array([[207.02, 239.02, 271.7 , 266.53],\n",
       "        [203.81, 155.07, 332.43, 198.  ]]),\n",
       " array([[229.71, 436.86, 291.6 , 464.99]]),\n",
       " array([[150.77, 135.62, 257.86, 152.95]]),\n",
       " array([[263.6 , 248.1 , 313.02, 264.7 ]]),\n",
       " array([[382.34, 108.47, 516.64, 329.78]]),\n",
       " array([[415.15, 195.7 , 440.6 , 303.63]]),\n",
       " array([[250.89, 140.54, 321.78, 183.42]]),\n",
       " array([[249.66, 101.23, 322.76, 228.04]]),\n",
       " array([[265.77, 320.64, 342.53, 365.78]]),\n",
       " array([[241.35, 127.25, 293.87, 191.66]]),\n",
       " array([[182.38, 282.77, 254.37, 318.77]]),\n",
       " array([[ 77.6 , 311.73, 143.15, 421.92]]),\n",
       " array([[218.28, 342.71, 293.83, 406.71]]),\n",
       " array([[296.82, 196.48, 351.15, 209.75]]),\n",
       " array([[319.88, 327.46, 395.32, 349.22]]),\n",
       " array([[114.91, 274.96, 151.85, 307.96]]),\n",
       " array([[350.56, 294.47, 461.66, 380.76]]),\n",
       " array([[421.34, 182.27, 460.97, 221.9 ]]),\n",
       " array([[246.18, 306.3 , 275.61, 325.2 ]]),\n",
       " array([[249.51, 296.8 , 307.19, 366.44],\n",
       "        [430.39, 154.16, 498.85, 210.23]]),\n",
       " array([[348.98, 224.25, 455.64, 253.18]]),\n",
       " array([[485.7 , 102.8 , 577.54, 205.87]]),\n",
       " array([[407.33, 259.58, 434.41, 305.09],\n",
       "        [249.98, 239.58, 273.98, 264.65]]),\n",
       " array([[338.18, 379.88, 379.25, 415.9 ]]),\n",
       " array([[250.18, 372.41, 267.47, 504.56]]),\n",
       " array([[271.84, 368.63, 298.05, 396.1 ]]),\n",
       " array([[375.39, 295.49, 481.2 , 337.12]]),\n",
       " array([[350.54, 250.92, 402.75, 284.75]]),\n",
       " array([[360.71, 249.43, 398.34, 282.5 ],\n",
       "        [278.  , 365.53, 332.2 , 389.38]]),\n",
       " array([[  2.02,  32.  , 209.82, 436.22]]),\n",
       " array([[  0.  , 267.32, 217.91, 316.3 ]]),\n",
       " array([[212.41, 229.5 , 328.18, 280.35]]),\n",
       " array([[433.83,  62.63, 634.24, 215.21]]),\n",
       " array([[188.24, 158.15, 314.25, 189.78]]),\n",
       " array([[295.58, 179.62, 332.4 , 224.96]]),\n",
       " array([[420.47, 241.39, 452.67, 328.88]]),\n",
       " array([[417.47, 230.37, 434.53, 256.6 ]]),\n",
       " array([[452.91, 231.79, 485.85, 243.9 ]]),\n",
       " array([[350.34, 228.83, 380.07, 252.31]]),\n",
       " array([[115.61, 547.93, 184.73, 603.59]]),\n",
       " array([[ 99.8 , 174.21, 159.38, 231.88]]),\n",
       " array([[271.  , 502.45, 294.31, 517.57]]),\n",
       " array([[148.26, 336.48, 176.  , 361.08]]),\n",
       " array([[4.1000e-01, 3.7250e+02, 2.2120e+01, 4.1507e+02]]),\n",
       " array([[238.18, 367.55, 317.69, 419.51]]),\n",
       " array([[264.52, 123.28, 272.62, 153.57]]),\n",
       " array([[365.09, 411.43, 405.45, 456.3 ]]),\n",
       " array([[255.9 , 213.67, 290.78, 231.77]]),\n",
       " array([[ 88.85, 178.33, 109.94, 199.94],\n",
       "        [176.5 , 122.99, 267.38, 205.19],\n",
       "        [478.91, 256.16, 566.48, 330.48]]),\n",
       " array([[184.57, 251.68, 238.46, 310.46]]),\n",
       " array([[357.92,  29.14, 465.13, 156.65]]),\n",
       " array([[246.53, 194.14, 301.13, 213.15]]),\n",
       " array([[188.09, 135.03, 305.78, 165.16]]),\n",
       " array([[221.24, 351.16, 233.94, 391.24]]),\n",
       " array([[193.87, 102.52, 227.28, 180.28]]),\n",
       " array([[440.56, 390.67, 490.07, 416.59]]),\n",
       " array([[ 96.67, 302.77, 161.53, 368.59]]),\n",
       " array([[133.06, 293.24, 219.19, 329.34]]),\n",
       " array([[ 27.51,  45.38, 132.03, 463.47]]),\n",
       " array([[166.97, 255.4 , 188.08, 306.3 ]]),\n",
       " array([[151.11, 241.79, 237.02, 281.28]]),\n",
       " array([[252.17, 354.89, 267.05, 383.47]]),\n",
       " array([[203.48, 219.42, 252.43, 270.57]]),\n",
       " array([[117.74, 330.37, 293.12, 378.28]]),\n",
       " array([[195.6 , 290.2 , 219.71, 323.36]])]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[np.array(eat_anno[\"boxes_o\"])[[eat_anno[\"actions\"] == np.ones_like(eat_anno[\"actions\"]) * 7]] for eat_anno in eat_annos]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "verb_classes = ['hold_obj', 'stand', 'sit_instr', 'ride_instr', 'walk', 'look_obj', 'hit_instr', 'hit_obj',\n",
    "'eat_obj', 'eat_instr', 'jump_instr', 'lay_instr', 'talk_on_phone_instr', 'carry_obj',\n",
    "'throw_obj', 'catch_obj', 'cut_instr', 'cut_obj', 'run', 'work_on_computer_instr',\n",
    "'ski_instr', 'surf_instr', 'skateboard_instr', 'smile', 'drink_instr', 'kick_obj',\n",
    "'point_instr', 'read_obj', 'snowboard_instr']\n",
    "len(verb_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29, 80)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corre_vcoco = np.load(\"/root/whzh/codes/HOI/upt/vcoco/v-coco/v-coco/annotations/cdn_annotations/corre_vcoco.npy\")\n",
    "corre_vcoco.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('hold_obj',\n",
       "  ['person',\n",
       "   'bicycle',\n",
       "   'car',\n",
       "   'motorcycle',\n",
       "   'airplane',\n",
       "   'bus',\n",
       "   'train',\n",
       "   'truck',\n",
       "   'boat',\n",
       "   'traffic light',\n",
       "   'fire hydrant',\n",
       "   'stop sign',\n",
       "   'parking meter',\n",
       "   'bench',\n",
       "   'bird',\n",
       "   'cat',\n",
       "   'dog',\n",
       "   'horse',\n",
       "   'sheep',\n",
       "   'cow',\n",
       "   'elephant',\n",
       "   'bear',\n",
       "   'zebra',\n",
       "   'giraffe',\n",
       "   'backpack',\n",
       "   'umbrella',\n",
       "   'handbag',\n",
       "   'tie',\n",
       "   'suitcase',\n",
       "   'frisbee',\n",
       "   'skis',\n",
       "   'snowboard',\n",
       "   'sports ball',\n",
       "   'kite',\n",
       "   'baseball bat',\n",
       "   'baseball glove',\n",
       "   'skateboard',\n",
       "   'surfboard',\n",
       "   'tennis racket',\n",
       "   'bottle',\n",
       "   'wine glass',\n",
       "   'cup',\n",
       "   'fork',\n",
       "   'knife',\n",
       "   'spoon',\n",
       "   'bowl',\n",
       "   'banana',\n",
       "   'apple',\n",
       "   'sandwich',\n",
       "   'orange',\n",
       "   'broccoli',\n",
       "   'carrot',\n",
       "   'hot dog',\n",
       "   'pizza',\n",
       "   'donut',\n",
       "   'cake',\n",
       "   'chair',\n",
       "   'couch',\n",
       "   'potted plant',\n",
       "   'bed',\n",
       "   'dining table',\n",
       "   'toilet',\n",
       "   'tv',\n",
       "   'laptop',\n",
       "   'mouse',\n",
       "   'remote',\n",
       "   'keyboard',\n",
       "   'cell phone',\n",
       "   'microwave',\n",
       "   'oven',\n",
       "   'toaster',\n",
       "   'sink',\n",
       "   'refrigerator',\n",
       "   'book',\n",
       "   'clock',\n",
       "   'vase',\n",
       "   'scissors',\n",
       "   'teddy bear',\n",
       "   'hair drier',\n",
       "   'toothbrush']),\n",
       " ('stand',\n",
       "  ['person',\n",
       "   'bicycle',\n",
       "   'car',\n",
       "   'motorcycle',\n",
       "   'airplane',\n",
       "   'bus',\n",
       "   'train',\n",
       "   'truck',\n",
       "   'boat',\n",
       "   'traffic light',\n",
       "   'fire hydrant',\n",
       "   'stop sign',\n",
       "   'parking meter',\n",
       "   'bench',\n",
       "   'bird',\n",
       "   'cat',\n",
       "   'dog',\n",
       "   'horse',\n",
       "   'sheep',\n",
       "   'cow',\n",
       "   'elephant',\n",
       "   'bear',\n",
       "   'zebra',\n",
       "   'giraffe',\n",
       "   'backpack',\n",
       "   'umbrella',\n",
       "   'handbag',\n",
       "   'tie',\n",
       "   'suitcase',\n",
       "   'frisbee',\n",
       "   'skis',\n",
       "   'snowboard',\n",
       "   'sports ball',\n",
       "   'kite',\n",
       "   'baseball bat',\n",
       "   'baseball glove',\n",
       "   'skateboard',\n",
       "   'surfboard',\n",
       "   'tennis racket',\n",
       "   'bottle',\n",
       "   'wine glass',\n",
       "   'cup',\n",
       "   'fork',\n",
       "   'knife',\n",
       "   'spoon',\n",
       "   'bowl',\n",
       "   'banana',\n",
       "   'apple',\n",
       "   'sandwich',\n",
       "   'orange',\n",
       "   'broccoli',\n",
       "   'carrot',\n",
       "   'hot dog',\n",
       "   'pizza',\n",
       "   'donut',\n",
       "   'cake',\n",
       "   'chair',\n",
       "   'couch',\n",
       "   'potted plant',\n",
       "   'bed',\n",
       "   'dining table',\n",
       "   'toilet',\n",
       "   'tv',\n",
       "   'laptop',\n",
       "   'mouse',\n",
       "   'remote',\n",
       "   'keyboard',\n",
       "   'cell phone',\n",
       "   'microwave',\n",
       "   'oven',\n",
       "   'toaster',\n",
       "   'sink',\n",
       "   'refrigerator',\n",
       "   'book',\n",
       "   'clock',\n",
       "   'vase',\n",
       "   'scissors',\n",
       "   'teddy bear',\n",
       "   'hair drier',\n",
       "   'toothbrush']),\n",
       " ('sit_instr',\n",
       "  ['bicycle',\n",
       "   'motorcycle',\n",
       "   'bench',\n",
       "   'horse',\n",
       "   'elephant',\n",
       "   'backpack',\n",
       "   'handbag',\n",
       "   'suitcase',\n",
       "   'chair',\n",
       "   'couch',\n",
       "   'bed',\n",
       "   'dining table',\n",
       "   'toilet']),\n",
       " ('ride_instr',\n",
       "  ['bicycle',\n",
       "   'car',\n",
       "   'motorcycle',\n",
       "   'airplane',\n",
       "   'bus',\n",
       "   'train',\n",
       "   'truck',\n",
       "   'boat',\n",
       "   'horse',\n",
       "   'elephant']),\n",
       " ('walk',\n",
       "  ['person',\n",
       "   'bicycle',\n",
       "   'car',\n",
       "   'motorcycle',\n",
       "   'airplane',\n",
       "   'bus',\n",
       "   'train',\n",
       "   'truck',\n",
       "   'boat',\n",
       "   'traffic light',\n",
       "   'fire hydrant',\n",
       "   'stop sign',\n",
       "   'parking meter',\n",
       "   'bench',\n",
       "   'bird',\n",
       "   'cat',\n",
       "   'dog',\n",
       "   'horse',\n",
       "   'sheep',\n",
       "   'cow',\n",
       "   'elephant',\n",
       "   'bear',\n",
       "   'zebra',\n",
       "   'giraffe',\n",
       "   'backpack',\n",
       "   'umbrella',\n",
       "   'handbag',\n",
       "   'tie',\n",
       "   'suitcase',\n",
       "   'frisbee',\n",
       "   'skis',\n",
       "   'snowboard',\n",
       "   'sports ball',\n",
       "   'kite',\n",
       "   'baseball bat',\n",
       "   'baseball glove',\n",
       "   'skateboard',\n",
       "   'surfboard',\n",
       "   'tennis racket',\n",
       "   'bottle',\n",
       "   'wine glass',\n",
       "   'cup',\n",
       "   'fork',\n",
       "   'knife',\n",
       "   'spoon',\n",
       "   'bowl',\n",
       "   'banana',\n",
       "   'apple',\n",
       "   'sandwich',\n",
       "   'orange',\n",
       "   'broccoli',\n",
       "   'carrot',\n",
       "   'hot dog',\n",
       "   'pizza',\n",
       "   'donut',\n",
       "   'cake',\n",
       "   'chair',\n",
       "   'couch',\n",
       "   'potted plant',\n",
       "   'bed',\n",
       "   'dining table',\n",
       "   'toilet',\n",
       "   'tv',\n",
       "   'laptop',\n",
       "   'mouse',\n",
       "   'remote',\n",
       "   'keyboard',\n",
       "   'cell phone',\n",
       "   'microwave',\n",
       "   'oven',\n",
       "   'toaster',\n",
       "   'sink',\n",
       "   'refrigerator',\n",
       "   'book',\n",
       "   'clock',\n",
       "   'vase',\n",
       "   'scissors',\n",
       "   'teddy bear',\n",
       "   'hair drier',\n",
       "   'toothbrush']),\n",
       " ('look_obj',\n",
       "  ['person',\n",
       "   'bicycle',\n",
       "   'car',\n",
       "   'motorcycle',\n",
       "   'airplane',\n",
       "   'bus',\n",
       "   'train',\n",
       "   'truck',\n",
       "   'boat',\n",
       "   'traffic light',\n",
       "   'fire hydrant',\n",
       "   'stop sign',\n",
       "   'parking meter',\n",
       "   'bench',\n",
       "   'bird',\n",
       "   'cat',\n",
       "   'dog',\n",
       "   'horse',\n",
       "   'sheep',\n",
       "   'cow',\n",
       "   'elephant',\n",
       "   'bear',\n",
       "   'zebra',\n",
       "   'giraffe',\n",
       "   'backpack',\n",
       "   'umbrella',\n",
       "   'handbag',\n",
       "   'tie',\n",
       "   'suitcase',\n",
       "   'frisbee',\n",
       "   'skis',\n",
       "   'snowboard',\n",
       "   'sports ball',\n",
       "   'kite',\n",
       "   'baseball bat',\n",
       "   'baseball glove',\n",
       "   'skateboard',\n",
       "   'surfboard',\n",
       "   'tennis racket',\n",
       "   'bottle',\n",
       "   'wine glass',\n",
       "   'cup',\n",
       "   'fork',\n",
       "   'knife',\n",
       "   'spoon',\n",
       "   'bowl',\n",
       "   'banana',\n",
       "   'apple',\n",
       "   'sandwich',\n",
       "   'orange',\n",
       "   'broccoli',\n",
       "   'carrot',\n",
       "   'hot dog',\n",
       "   'pizza',\n",
       "   'donut',\n",
       "   'cake',\n",
       "   'chair',\n",
       "   'couch',\n",
       "   'potted plant',\n",
       "   'bed',\n",
       "   'dining table',\n",
       "   'toilet',\n",
       "   'tv',\n",
       "   'laptop',\n",
       "   'mouse',\n",
       "   'remote',\n",
       "   'keyboard',\n",
       "   'cell phone',\n",
       "   'microwave',\n",
       "   'oven',\n",
       "   'toaster',\n",
       "   'sink',\n",
       "   'refrigerator',\n",
       "   'book',\n",
       "   'clock',\n",
       "   'vase',\n",
       "   'scissors',\n",
       "   'teddy bear',\n",
       "   'hair drier',\n",
       "   'toothbrush']),\n",
       " ('hit_instr', ['baseball bat', 'tennis racket']),\n",
       " ('hit_obj', ['sports ball']),\n",
       " ('eat_obj',\n",
       "  ['banana',\n",
       "   'apple',\n",
       "   'sandwich',\n",
       "   'orange',\n",
       "   'broccoli',\n",
       "   'carrot',\n",
       "   'hot dog',\n",
       "   'pizza',\n",
       "   'donut',\n",
       "   'cake']),\n",
       " ('eat_instr', ['fork', 'knife', 'spoon']),\n",
       " ('jump_instr', ['skis', 'snowboard', 'skateboard', 'surfboard']),\n",
       " ('lay_instr', ['bench', 'chair', 'couch', 'bed', 'dining table', 'toilet']),\n",
       " ('talk_on_phone_instr', ['cell phone']),\n",
       " ('carry_obj',\n",
       "  ['person',\n",
       "   'bicycle',\n",
       "   'car',\n",
       "   'motorcycle',\n",
       "   'airplane',\n",
       "   'bus',\n",
       "   'train',\n",
       "   'truck',\n",
       "   'boat',\n",
       "   'traffic light',\n",
       "   'fire hydrant',\n",
       "   'stop sign',\n",
       "   'parking meter',\n",
       "   'bench',\n",
       "   'bird',\n",
       "   'cat',\n",
       "   'dog',\n",
       "   'horse',\n",
       "   'sheep',\n",
       "   'cow',\n",
       "   'elephant',\n",
       "   'bear',\n",
       "   'zebra',\n",
       "   'giraffe',\n",
       "   'backpack',\n",
       "   'umbrella',\n",
       "   'handbag',\n",
       "   'tie',\n",
       "   'suitcase',\n",
       "   'frisbee',\n",
       "   'skis',\n",
       "   'snowboard',\n",
       "   'sports ball',\n",
       "   'kite',\n",
       "   'baseball bat',\n",
       "   'baseball glove',\n",
       "   'skateboard',\n",
       "   'surfboard',\n",
       "   'tennis racket',\n",
       "   'bottle',\n",
       "   'wine glass',\n",
       "   'cup',\n",
       "   'fork',\n",
       "   'knife',\n",
       "   'spoon',\n",
       "   'bowl',\n",
       "   'banana',\n",
       "   'apple',\n",
       "   'sandwich',\n",
       "   'orange',\n",
       "   'broccoli',\n",
       "   'carrot',\n",
       "   'hot dog',\n",
       "   'pizza',\n",
       "   'donut',\n",
       "   'cake',\n",
       "   'chair',\n",
       "   'couch',\n",
       "   'potted plant',\n",
       "   'bed',\n",
       "   'dining table',\n",
       "   'toilet',\n",
       "   'tv',\n",
       "   'laptop',\n",
       "   'mouse',\n",
       "   'remote',\n",
       "   'keyboard',\n",
       "   'cell phone',\n",
       "   'microwave',\n",
       "   'oven',\n",
       "   'toaster',\n",
       "   'sink',\n",
       "   'refrigerator',\n",
       "   'book',\n",
       "   'clock',\n",
       "   'vase',\n",
       "   'scissors',\n",
       "   'teddy bear',\n",
       "   'hair drier',\n",
       "   'toothbrush']),\n",
       " ('throw_obj', ['frisbee', 'sports ball']),\n",
       " ('catch_obj', ['frisbee', 'sports ball']),\n",
       " ('cut_instr', ['fork', 'knife', 'scissors']),\n",
       " ('cut_obj',\n",
       "  ['person',\n",
       "   'bicycle',\n",
       "   'car',\n",
       "   'motorcycle',\n",
       "   'airplane',\n",
       "   'bus',\n",
       "   'train',\n",
       "   'truck',\n",
       "   'boat',\n",
       "   'traffic light',\n",
       "   'fire hydrant',\n",
       "   'stop sign',\n",
       "   'parking meter',\n",
       "   'bench',\n",
       "   'bird',\n",
       "   'cat',\n",
       "   'dog',\n",
       "   'horse',\n",
       "   'sheep',\n",
       "   'cow',\n",
       "   'elephant',\n",
       "   'bear',\n",
       "   'zebra',\n",
       "   'giraffe',\n",
       "   'backpack',\n",
       "   'umbrella',\n",
       "   'handbag',\n",
       "   'tie',\n",
       "   'suitcase',\n",
       "   'frisbee',\n",
       "   'skis',\n",
       "   'snowboard',\n",
       "   'sports ball',\n",
       "   'kite',\n",
       "   'baseball bat',\n",
       "   'baseball glove',\n",
       "   'skateboard',\n",
       "   'surfboard',\n",
       "   'tennis racket',\n",
       "   'bottle',\n",
       "   'wine glass',\n",
       "   'cup',\n",
       "   'fork',\n",
       "   'knife',\n",
       "   'spoon',\n",
       "   'bowl',\n",
       "   'banana',\n",
       "   'apple',\n",
       "   'sandwich',\n",
       "   'orange',\n",
       "   'broccoli',\n",
       "   'carrot',\n",
       "   'hot dog',\n",
       "   'pizza',\n",
       "   'donut',\n",
       "   'cake',\n",
       "   'chair',\n",
       "   'couch',\n",
       "   'potted plant',\n",
       "   'bed',\n",
       "   'dining table',\n",
       "   'toilet',\n",
       "   'tv',\n",
       "   'laptop',\n",
       "   'mouse',\n",
       "   'remote',\n",
       "   'keyboard',\n",
       "   'cell phone',\n",
       "   'microwave',\n",
       "   'oven',\n",
       "   'toaster',\n",
       "   'sink',\n",
       "   'refrigerator',\n",
       "   'book',\n",
       "   'clock',\n",
       "   'vase',\n",
       "   'scissors',\n",
       "   'teddy bear',\n",
       "   'hair drier',\n",
       "   'toothbrush']),\n",
       " ('run',\n",
       "  ['person',\n",
       "   'bicycle',\n",
       "   'car',\n",
       "   'motorcycle',\n",
       "   'airplane',\n",
       "   'bus',\n",
       "   'train',\n",
       "   'truck',\n",
       "   'boat',\n",
       "   'traffic light',\n",
       "   'fire hydrant',\n",
       "   'stop sign',\n",
       "   'parking meter',\n",
       "   'bench',\n",
       "   'bird',\n",
       "   'cat',\n",
       "   'dog',\n",
       "   'horse',\n",
       "   'sheep',\n",
       "   'cow',\n",
       "   'elephant',\n",
       "   'bear',\n",
       "   'zebra',\n",
       "   'giraffe',\n",
       "   'backpack',\n",
       "   'umbrella',\n",
       "   'handbag',\n",
       "   'tie',\n",
       "   'suitcase',\n",
       "   'frisbee',\n",
       "   'skis',\n",
       "   'snowboard',\n",
       "   'sports ball',\n",
       "   'kite',\n",
       "   'baseball bat',\n",
       "   'baseball glove',\n",
       "   'skateboard',\n",
       "   'surfboard',\n",
       "   'tennis racket',\n",
       "   'bottle',\n",
       "   'wine glass',\n",
       "   'cup',\n",
       "   'fork',\n",
       "   'knife',\n",
       "   'spoon',\n",
       "   'bowl',\n",
       "   'banana',\n",
       "   'apple',\n",
       "   'sandwich',\n",
       "   'orange',\n",
       "   'broccoli',\n",
       "   'carrot',\n",
       "   'hot dog',\n",
       "   'pizza',\n",
       "   'donut',\n",
       "   'cake',\n",
       "   'chair',\n",
       "   'couch',\n",
       "   'potted plant',\n",
       "   'bed',\n",
       "   'dining table',\n",
       "   'toilet',\n",
       "   'tv',\n",
       "   'laptop',\n",
       "   'mouse',\n",
       "   'remote',\n",
       "   'keyboard',\n",
       "   'cell phone',\n",
       "   'microwave',\n",
       "   'oven',\n",
       "   'toaster',\n",
       "   'sink',\n",
       "   'refrigerator',\n",
       "   'book',\n",
       "   'clock',\n",
       "   'vase',\n",
       "   'scissors',\n",
       "   'teddy bear',\n",
       "   'hair drier',\n",
       "   'toothbrush']),\n",
       " ('work_on_computer_instr', ['laptop']),\n",
       " ('ski_instr', ['skis']),\n",
       " ('surf_instr', ['surfboard']),\n",
       " ('skateboard_instr', ['skateboard']),\n",
       " ('smile',\n",
       "  ['person',\n",
       "   'bicycle',\n",
       "   'car',\n",
       "   'motorcycle',\n",
       "   'airplane',\n",
       "   'bus',\n",
       "   'train',\n",
       "   'truck',\n",
       "   'boat',\n",
       "   'traffic light',\n",
       "   'fire hydrant',\n",
       "   'stop sign',\n",
       "   'parking meter',\n",
       "   'bench',\n",
       "   'bird',\n",
       "   'cat',\n",
       "   'dog',\n",
       "   'horse',\n",
       "   'sheep',\n",
       "   'cow',\n",
       "   'elephant',\n",
       "   'bear',\n",
       "   'zebra',\n",
       "   'giraffe',\n",
       "   'backpack',\n",
       "   'umbrella',\n",
       "   'handbag',\n",
       "   'tie',\n",
       "   'suitcase',\n",
       "   'frisbee',\n",
       "   'skis',\n",
       "   'snowboard',\n",
       "   'sports ball',\n",
       "   'kite',\n",
       "   'baseball bat',\n",
       "   'baseball glove',\n",
       "   'skateboard',\n",
       "   'surfboard',\n",
       "   'tennis racket',\n",
       "   'bottle',\n",
       "   'wine glass',\n",
       "   'cup',\n",
       "   'fork',\n",
       "   'knife',\n",
       "   'spoon',\n",
       "   'bowl',\n",
       "   'banana',\n",
       "   'apple',\n",
       "   'sandwich',\n",
       "   'orange',\n",
       "   'broccoli',\n",
       "   'carrot',\n",
       "   'hot dog',\n",
       "   'pizza',\n",
       "   'donut',\n",
       "   'cake',\n",
       "   'chair',\n",
       "   'couch',\n",
       "   'potted plant',\n",
       "   'bed',\n",
       "   'dining table',\n",
       "   'toilet',\n",
       "   'tv',\n",
       "   'laptop',\n",
       "   'mouse',\n",
       "   'remote',\n",
       "   'keyboard',\n",
       "   'cell phone',\n",
       "   'microwave',\n",
       "   'oven',\n",
       "   'toaster',\n",
       "   'sink',\n",
       "   'refrigerator',\n",
       "   'book',\n",
       "   'clock',\n",
       "   'vase',\n",
       "   'scissors',\n",
       "   'teddy bear',\n",
       "   'hair drier',\n",
       "   'toothbrush']),\n",
       " ('drink_instr', ['bottle', 'wine glass', 'cup', 'bowl']),\n",
       " ('kick_obj', ['sports ball']),\n",
       " ('point_instr',\n",
       "  ['person',\n",
       "   'bicycle',\n",
       "   'car',\n",
       "   'motorcycle',\n",
       "   'airplane',\n",
       "   'bus',\n",
       "   'train',\n",
       "   'truck',\n",
       "   'boat',\n",
       "   'traffic light',\n",
       "   'fire hydrant',\n",
       "   'stop sign',\n",
       "   'parking meter',\n",
       "   'bench',\n",
       "   'bird',\n",
       "   'cat',\n",
       "   'dog',\n",
       "   'horse',\n",
       "   'sheep',\n",
       "   'cow',\n",
       "   'elephant',\n",
       "   'bear',\n",
       "   'zebra',\n",
       "   'giraffe',\n",
       "   'backpack',\n",
       "   'umbrella',\n",
       "   'handbag',\n",
       "   'tie',\n",
       "   'suitcase',\n",
       "   'frisbee',\n",
       "   'skis',\n",
       "   'snowboard',\n",
       "   'sports ball',\n",
       "   'kite',\n",
       "   'baseball bat',\n",
       "   'baseball glove',\n",
       "   'skateboard',\n",
       "   'surfboard',\n",
       "   'tennis racket',\n",
       "   'bottle',\n",
       "   'wine glass',\n",
       "   'cup',\n",
       "   'fork',\n",
       "   'knife',\n",
       "   'spoon',\n",
       "   'bowl',\n",
       "   'banana',\n",
       "   'apple',\n",
       "   'sandwich',\n",
       "   'orange',\n",
       "   'broccoli',\n",
       "   'carrot',\n",
       "   'hot dog',\n",
       "   'pizza',\n",
       "   'donut',\n",
       "   'cake',\n",
       "   'chair',\n",
       "   'couch',\n",
       "   'potted plant',\n",
       "   'bed',\n",
       "   'dining table',\n",
       "   'toilet',\n",
       "   'tv',\n",
       "   'laptop',\n",
       "   'mouse',\n",
       "   'remote',\n",
       "   'keyboard',\n",
       "   'cell phone',\n",
       "   'microwave',\n",
       "   'oven',\n",
       "   'toaster',\n",
       "   'sink',\n",
       "   'refrigerator',\n",
       "   'book',\n",
       "   'clock',\n",
       "   'vase',\n",
       "   'scissors',\n",
       "   'teddy bear',\n",
       "   'hair drier',\n",
       "   'toothbrush']),\n",
       " ('read_obj', ['book']),\n",
       " ('snowboard_instr', ['snowboard'])]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(verb_classes[i], list(np.array(OBJECTS)[cor.astype(np.bool)]) ) for i, cor in enumerate(corre_vcoco)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corre_vcoco[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "version = 'trainval'\n",
    "anno_file = \"/root/whzh/codes/HOI/upt/vcoco/origin_anno/instances_vcoco_{}.json\".format(version)\n",
    "with open(anno_file, 'r') as f:\n",
    "    hicos = json.load(f)\n",
    "anno_file = \"/root/whzh/codes/HOI/upt/vcoco/v-coco/v-coco/annotations/cdn_annotations/{}_vcoco.json\".format(version)\n",
    "with open(anno_file, 'r') as f:\n",
    "    hicos_2 = json.load(f)\n",
    "    \n",
    "save_hico = dict(annotations=None, classes=None, objects=None, images=None, action_to_object=None)\n",
    "save_hico['classes'] = ['hold obj', 'stand', 'sit instr', 'ride instr', 'walk', 'look obj', 'hit instr', 'hit obj',\n",
    "                        'eat obj', 'eat instr', 'jump instr', 'lay instr', 'talk_on_phone instr', 'carry obj',\n",
    "                        'throw obj', 'catch obj', 'cut instr', 'cut obj', 'run', 'work_on_computer instr',\n",
    "                        'ski instr', 'surf instr', 'skateboard instr', 'smile', 'drink instr', 'kick obj',\n",
    "                        'point instr', 'read obj', 'snowboard instr']\n",
    "save_hico['objects'] = hicos['objects']\n",
    "save_hico['images'] = [int(hico['file_name'].split('_')[-1].split('.')[0])   for hico in hicos_2]\n",
    "corre_vcoco = np.load('/root/whzh/codes/HOI/upt/vcoco/v-coco/v-coco/annotations/cdn_annotations/corre_vcoco.npy')\n",
    "save_hico['action_to_object'] = [(np.where(cor==1)[0]+1).tolist() for cor in corre_vcoco]\n",
    "\n",
    "save_hico['annotations'] = list()\n",
    "temp = dict()\n",
    "for hico in hicos_2:\n",
    "    temp[int(hico['file_name'].split('_')[-1].split('.')[0])] = hico\n",
    "    \n",
    "for img in save_hico['images']:\n",
    "    x = dict()\n",
    "    t = temp[img]\n",
    "    x['file_name'] = t['file_name']\n",
    "    x['boxes_h'] = [list(np.array(t['annotations'])[i['subject_id']]['bbox']) for i in t['hoi_annotation']]\n",
    "    x['boxes_o'] = [list(np.array(t['annotations'])[i['object_id']]['bbox']) if i['object_id']!=-1 else \n",
    "                    list(np.array(t['annotations'])[i['subject_id']]['bbox'])\n",
    "                    for i in t['hoi_annotation']]\n",
    "    x['actions'] = [i['category_id'] for i in t['hoi_annotation']]\n",
    "\n",
    "    x['objects'] = [int(np.array(t['annotations'])[i['object_id']]['category_id']) if i['object_id']!=-1 else \n",
    "                    int(np.array(t['annotations'])[i['subject_id']]['category_id'])\n",
    "                    for i in t['hoi_annotation']]\n",
    "    cov = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13,\n",
    "        14, 15, 16, 17, 18, 19, 20, 21, 22, 23,\n",
    "        24, 25, 27, 28, 31, 32, 33, 34, 35, 36,\n",
    "        37, 38, 39, 40, 41, 42, 43, 44, 46, 47,\n",
    "        48, 49, 50, 51, 52, 53, 54, 55, 56, 57,\n",
    "        58, 59, 60, 61, 62, 63, 64, 65, 67, 70,\n",
    "        72, 73, 74, 75, 76, 77, 78, 79, 80, 81,\n",
    "        82, 84, 85, 86, 87, 88, 89, 90]\n",
    "    x['objects'] = [int(np.where([i==j for i in cov])[0]+1) for j in x['objects']]\n",
    "    save_hico['annotations'].append(x)\n",
    "anno_file = \"/root/whzh/codes/HOI/upt/vcoco/instances_vcoco_{}.json\".format(version)\n",
    "with open(anno_file, 'w') as f:\n",
    "    json.dump(save_hico, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9546\n"
     ]
    }
   ],
   "source": [
    "anno_file = \"/root/whzh/codes/HOI/cdn/data/hico_20160224_det/annotations/file_name_to_obj_cat.json\"\n",
    "with open(anno_file, 'r') as f:\n",
    "    file_name_to_obj_cat = json.load(f)\n",
    "print(len(file_name_to_obj_cat.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train_dataset = DataFactory(name='hicodet', partition='train2015', data_root='hicodet')\n",
    "test_dataset = DataFactory(name='hicodet', partition='test2015', data_root='hicodet')\n",
    "cov = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13,\n",
    "    14, 15, 16, 17, 18, 19, 20, 21, 22, 23,\n",
    "    24, 25, 27, 28, 31, 32, 33, 34, 35, 36,\n",
    "    37, 38, 39, 40, 41, 42, 43, 44, 46, 47,\n",
    "    48, 49, 50, 51, 52, 53, 54, 55, 56, 57,\n",
    "    58, 59, 60, 61, 62, 63, 64, 65, 67, 70,\n",
    "    72, 73, 74, 75, 76, 77, 78, 79, 80, 81,\n",
    "    82, 84, 85, 86, 87, 88, 89, 90]\n",
    "OBJECTS = [\n",
    "    'person', \n",
    "    'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', \n",
    "    'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', \n",
    "    'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', \n",
    "    'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', \n",
    "    'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', \n",
    "    'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', \n",
    "    'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven',\n",
    "    'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear','hair drier', 'toothbrush']\n",
    "hico_verb_name_dict =  {0: 'adjust', 1: 'assemble', 2: 'block', 3: 'blow', 4: 'board', 5: 'break', 6: 'brush_with', 7: 'buy', 8: 'carry', 9: 'catch', \n",
    "                        10: 'chase', 11: 'check', 12: 'clean', 13: 'control', 14: 'cook', 15: 'cut', 16: 'cut_with', 17: 'direct', 18: 'drag', 19: 'dribble', \n",
    "                        20: 'drink_with', 21: 'drive', 22: 'dry', 23: 'eat', 24: 'eat_at', 25: 'exit', 26: 'feed', 27: 'fill', 28: 'flip', 29: 'flush', \n",
    "                        30: 'fly', 31: 'greet', 32: 'grind', 33: 'groom', 34: 'herd', 35: 'hit', 36: 'hold', 37: 'hop_on', 38: 'hose', 39: 'hug', \n",
    "                        40: 'hunt', 41: 'inspect', 42: 'install', 43: 'jump', 44: 'kick', 45: 'kiss', 46: 'lasso', 47: 'launch', 48: 'lick', 49: 'lie_on', \n",
    "                        50: 'lift', 51: 'light', 52: 'load', 53: 'lose', 54: 'make', 55: 'milk', 56: 'move', 57: 'no_interaction', 58: 'open', 59: 'operate', \n",
    "                        60: 'pack', 61: 'paint', 62: 'park', 63: 'pay', 64: 'peel', 65: 'pet', 66: 'pick', 67: 'pick_up', 68: 'point', 69: 'pour', \n",
    "                        70: 'pull', 71: 'push', 72: 'race', 73: 'read', 74: 'release', 75: 'repair', 76: 'ride', 77: 'row', 78: 'run', 79: 'sail', \n",
    "                        80: 'scratch', 81: 'serve', 82: 'set', 83: 'shear', 84: 'sign', 85: 'sip', 86: 'sit_at', 87: 'sit_on', 88: 'slide', 89: 'smell', \n",
    "                        90: 'spin', 91: 'squeeze', 92: 'stab', 93: 'stand_on', 94: 'stand_under', 95: 'stick', 96: 'stir', 97: 'stop_at', 98: 'straddle', 99: 'swing', \n",
    "                        100: 'tag', 101: 'talk_on', 102: 'teach', 103: 'text_on', 104: 'throw', 105: 'tie', 106: 'toast', 107: 'train', 108: 'turn', 109: 'type_on', \n",
    "                        110: 'walk', 111: 'wash', 112: 'watch', 113: 'wave', 114: 'wear', 115: 'wield', 116: 'zip'}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9546\n"
     ]
    }
   ],
   "source": [
    "len(test_dataset.dataset._filenames)\n",
    "print(len(file_name_to_obj_cat.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "hoi_list = json.load(open(os.path.join('hicodet/hoi_list_new.json'), \"r\"))\n",
    "print(len(hoi_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'hicodet_vcoco/instances_test2015.json'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_103131/2039777091.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhoi_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'hicodet_vcoco/instances_test2015.json'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mcdn_hoi_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'hicodet/hico_20160224_det/annotations/test_hico.json'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'hicodet_vcoco/instances_test2015.json'"
     ]
    }
   ],
   "source": [
    "\n",
    "hoi_test = json.load(open(os.path.join('hicodet_vcoco/instances_test2015.json'), \"r\"))\n",
    "cdn_hoi_test = json.load(open(os.path.join('hicodet/hico_20160224_det/annotations/test_hico.json'), \"r\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['annotation', 'filenames', 'empty', 'objects', 'verbs', 'correspondence', 'size'])\n",
      "9658\n",
      "9546\n"
     ]
    }
   ],
   "source": [
    "print(hoi_test.keys())\n",
    "print(len(hoi_test['annotation']))\n",
    "print(len(cdn_hoi_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 4, 4], [1, 4, 17], [2, 4, 25], [3, 4, 30], [4, 4, 41], [5, 4, 52], [6, 4, 76], [7, 4, 87], [8, 4, 111], [9, 4, 57], [10, 1, 8], [11, 1, 36], [12, 1, 41], [13, 1, 43], [14, 1, 37], [15, 1, 62], [16, 1, 71], [17, 1, 75], [18, 1, 76], [19, 1, 87], [20, 1, 98], [21, 1, 110], [22, 1, 111], [23, 1, 57], [24, 14, 10], [25, 14, 26], [26, 14, 36], [27, 14, 65], [28, 14, 74], [29, 14, 112], [30, 14, 57], [31, 8, 4], [32, 8, 21], [33, 8, 25], [34, 8, 41], [35, 8, 43], [36, 8, 47], [37, 8, 75], [38, 8, 76], [39, 8, 77], [40, 8, 79], [41, 8, 87], [42, 8, 93], [43, 8, 105], [44, 8, 111], [45, 8, 57], [46, 39, 8], [47, 39, 20], [48, 39, 36], [49, 39, 41], [50, 39, 48], [51, 39, 58], [52, 39, 69], [53, 39, 57], [54, 5, 4], [55, 5, 17], [56, 5, 21], [57, 5, 25], [58, 5, 41], [59, 5, 52], [60, 5, 76], [61, 5, 87], [62, 5, 111], [63, 5, 113], [64, 5, 57], [65, 2, 4], [66, 2, 17], [67, 2, 21], [68, 2, 38], [69, 2, 41], [70, 2, 43], [71, 2, 52], [72, 2, 62], [73, 2, 76], [74, 2, 111], [75, 2, 57], [76, 15, 22], [77, 15, 26], [78, 15, 36], [79, 15, 39], [80, 15, 45], [81, 15, 65], [82, 15, 80], [83, 15, 111], [84, 15, 10], [85, 15, 57], [86, 56, 8], [87, 56, 36], [88, 56, 49], [89, 56, 87], [90, 56, 93], [91, 56, 57], [92, 57, 8], [93, 57, 49], [94, 57, 87], [95, 57, 57], [96, 19, 26], [97, 19, 34], [98, 19, 36], [99, 19, 39], [100, 19, 45], [101, 19, 46], [102, 19, 55], [103, 19, 65], [104, 19, 76], [105, 19, 110], [106, 19, 57], [107, 60, 12], [108, 60, 24], [109, 60, 86], [110, 60, 57], [111, 16, 8], [112, 16, 22], [113, 16, 26], [114, 16, 33], [115, 16, 36], [116, 16, 38], [117, 16, 39], [118, 16, 41], [119, 16, 45], [120, 16, 65], [121, 16, 78], [122, 16, 80], [123, 16, 98], [124, 16, 107], [125, 16, 110], [126, 16, 111], [127, 16, 10], [128, 16, 57], [129, 17, 26], [130, 17, 33], [131, 17, 36], [132, 17, 39], [133, 17, 43], [134, 17, 45], [135, 17, 52], [136, 17, 37], [137, 17, 65], [138, 17, 72], [139, 17, 76], [140, 17, 78], [141, 17, 98], [142, 17, 107], [143, 17, 110], [144, 17, 111], [145, 17, 57], [146, 3, 36], [147, 3, 41], [148, 3, 43], [149, 3, 37], [150, 3, 62], [151, 3, 71], [152, 3, 72], [153, 3, 76], [154, 3, 87], [155, 3, 98], [156, 3, 108], [157, 3, 110], [158, 3, 111], [159, 3, 57], [160, 0, 8], [161, 0, 31], [162, 0, 36], [163, 0, 39], [164, 0, 45], [165, 0, 92], [166, 0, 100], [167, 0, 102], [168, 0, 48], [169, 0, 57], [170, 58, 8], [171, 58, 36], [172, 58, 38], [173, 58, 57], [174, 18, 8], [175, 18, 26], [176, 18, 34], [177, 18, 36], [178, 18, 39], [179, 18, 45], [180, 18, 65], [181, 18, 76], [182, 18, 83], [183, 18, 110], [184, 18, 111], [185, 18, 57], [186, 6, 4], [187, 6, 21], [188, 6, 25], [189, 6, 52], [190, 6, 76], [191, 6, 87], [192, 6, 111], [193, 6, 57], [194, 62, 13], [195, 62, 75], [196, 62, 112], [197, 62, 57], [198, 47, 7], [199, 47, 15], [200, 47, 23], [201, 47, 36], [202, 47, 41], [203, 47, 64], [204, 47, 66], [205, 47, 89], [206, 47, 111], [207, 47, 57], [208, 24, 8], [209, 24, 36], [210, 24, 41], [211, 24, 58], [212, 24, 114], [213, 24, 57], [214, 46, 7], [215, 46, 8], [216, 46, 15], [217, 46, 23], [218, 46, 36], [219, 46, 41], [220, 46, 64], [221, 46, 66], [222, 46, 89], [223, 46, 57], [224, 34, 5], [225, 34, 8], [226, 34, 36], [227, 34, 84], [228, 34, 99], [229, 34, 104], [230, 34, 115], [231, 34, 57], [232, 35, 36], [233, 35, 114], [234, 35, 57], [235, 21, 26], [236, 21, 40], [237, 21, 112], [238, 21, 57], [239, 59, 12], [240, 59, 49], [241, 59, 87], [242, 59, 57], [243, 13, 41], [244, 13, 49], [245, 13, 87], [246, 13, 57], [247, 73, 8], [248, 73, 36], [249, 73, 58], [250, 73, 73], [251, 73, 57], [252, 45, 36], [253, 45, 96], [254, 45, 111], [255, 45, 48], [256, 45, 57], [257, 50, 15], [258, 50, 23], [259, 50, 36], [260, 50, 89], [261, 50, 96], [262, 50, 111], [263, 50, 57], [264, 55, 3], [265, 55, 8], [266, 55, 15], [267, 55, 23], [268, 55, 36], [269, 55, 51], [270, 55, 54], [271, 55, 67], [272, 55, 57], [273, 51, 8], [274, 51, 14], [275, 51, 15], [276, 51, 23], [277, 51, 36], [278, 51, 64], [279, 51, 89], [280, 51, 96], [281, 51, 111], [282, 51, 57], [283, 67, 8], [284, 67, 36], [285, 67, 73], [286, 67, 75], [287, 67, 101], [288, 67, 103], [289, 67, 57], [290, 74, 11], [291, 74, 36], [292, 74, 75], [293, 74, 82], [294, 74, 57], [295, 41, 8], [296, 41, 20], [297, 41, 36], [298, 41, 41], [299, 41, 69], [300, 41, 85], [301, 41, 89], [302, 41, 27], [303, 41, 111], [304, 41, 57], [305, 54, 7], [306, 54, 8], [307, 54, 23], [308, 54, 36], [309, 54, 54], [310, 54, 67], [311, 54, 89], [312, 54, 57], [313, 20, 26], [314, 20, 36], [315, 20, 38], [316, 20, 39], [317, 20, 45], [318, 20, 37], [319, 20, 65], [320, 20, 76], [321, 20, 110], [322, 20, 111], [323, 20, 112], [324, 20, 57], [325, 10, 39], [326, 10, 41], [327, 10, 58], [328, 10, 61], [329, 10, 57], [330, 42, 36], [331, 42, 50], [332, 42, 95], [333, 42, 48], [334, 42, 111], [335, 42, 57], [336, 29, 2], [337, 29, 9], [338, 29, 36], [339, 29, 90], [340, 29, 104], [341, 29, 57], [342, 23, 26], [343, 23, 45], [344, 23, 65], [345, 23, 76], [346, 23, 112], [347, 23, 57], [348, 78, 36], [349, 78, 59], [350, 78, 75], [351, 78, 57], [352, 26, 8], [353, 26, 36], [354, 26, 41], [355, 26, 57], [356, 52, 8], [357, 52, 14], [358, 52, 15], [359, 52, 23], [360, 52, 36], [361, 52, 54], [362, 52, 57], [363, 66, 8], [364, 66, 12], [365, 66, 36], [366, 66, 109], [367, 66, 57], [368, 33, 1], [369, 33, 8], [370, 33, 30], [371, 33, 36], [372, 33, 41], [373, 33, 47], [374, 33, 70], [375, 33, 57], [376, 43, 16], [377, 43, 36], [378, 43, 95], [379, 43, 111], [380, 43, 115], [381, 43, 48], [382, 43, 57], [383, 63, 36], [384, 63, 58], [385, 63, 73], [386, 63, 75], [387, 63, 109], [388, 63, 57], [389, 68, 12], [390, 68, 58], [391, 68, 59], [392, 68, 57], [393, 64, 13], [394, 64, 36], [395, 64, 75], [396, 64, 57], [397, 49, 7], [398, 49, 15], [399, 49, 23], [400, 49, 36], [401, 49, 41], [402, 49, 64], [403, 49, 66], [404, 49, 91], [405, 49, 111], [406, 49, 57], [407, 69, 12], [408, 69, 36], [409, 69, 41], [410, 69, 58], [411, 69, 75], [412, 69, 59], [413, 69, 57], [414, 12, 11], [415, 12, 63], [416, 12, 75], [417, 12, 57], [418, 53, 7], [419, 53, 8], [420, 53, 14], [421, 53, 15], [422, 53, 23], [423, 53, 36], [424, 53, 54], [425, 53, 67], [426, 53, 88], [427, 53, 89], [428, 53, 57], [429, 72, 12], [430, 72, 36], [431, 72, 56], [432, 72, 58], [433, 72, 57], [434, 65, 36], [435, 65, 68], [436, 65, 99], [437, 65, 57], [438, 48, 8], [439, 48, 14], [440, 48, 15], [441, 48, 23], [442, 48, 36], [443, 48, 54], [444, 48, 57], [445, 76, 16], [446, 76, 36], [447, 76, 58], [448, 76, 57], [449, 71, 12], [450, 71, 75], [451, 71, 111], [452, 71, 57], [453, 36, 8], [454, 36, 28], [455, 36, 32], [456, 36, 36], [457, 36, 43], [458, 36, 67], [459, 36, 76], [460, 36, 87], [461, 36, 93], [462, 36, 57], [463, 30, 0], [464, 30, 8], [465, 30, 36], [466, 30, 41], [467, 30, 43], [468, 30, 67], [469, 30, 75], [470, 30, 76], [471, 30, 93], [472, 30, 114], [473, 30, 57], [474, 31, 0], [475, 31, 8], [476, 31, 32], [477, 31, 36], [478, 31, 43], [479, 31, 76], [480, 31, 93], [481, 31, 114], [482, 31, 57], [483, 44, 36], [484, 44, 48], [485, 44, 111], [486, 44, 85], [487, 44, 57], [488, 32, 2], [489, 32, 8], [490, 32, 9], [491, 32, 19], [492, 32, 35], [493, 32, 36], [494, 32, 41], [495, 32, 44], [496, 32, 67], [497, 32, 81], [498, 32, 84], [499, 32, 90], [500, 32, 104], [501, 32, 57], [502, 11, 36], [503, 11, 94], [504, 11, 97], [505, 11, 57], [506, 28, 8], [507, 28, 18], [508, 28, 36], [509, 28, 39], [510, 28, 52], [511, 28, 58], [512, 28, 60], [513, 28, 67], [514, 28, 116], [515, 28, 57], [516, 37, 8], [517, 37, 18], [518, 37, 36], [519, 37, 41], [520, 37, 43], [521, 37, 49], [522, 37, 52], [523, 37, 76], [524, 37, 93], [525, 37, 87], [526, 37, 111], [527, 37, 57], [528, 77, 8], [529, 77, 36], [530, 77, 39], [531, 77, 45], [532, 77, 57], [533, 38, 8], [534, 38, 36], [535, 38, 41], [536, 38, 99], [537, 38, 57], [538, 27, 0], [539, 27, 15], [540, 27, 36], [541, 27, 41], [542, 27, 70], [543, 27, 105], [544, 27, 114], [545, 27, 57], [546, 70, 36], [547, 70, 59], [548, 70, 75], [549, 70, 57], [550, 61, 12], [551, 61, 29], [552, 61, 58], [553, 61, 75], [554, 61, 87], [555, 61, 93], [556, 61, 111], [557, 61, 57], [558, 79, 6], [559, 79, 36], [560, 79, 111], [561, 79, 57], [562, 9, 42], [563, 9, 75], [564, 9, 94], [565, 9, 97], [566, 9, 57], [567, 7, 17], [568, 7, 21], [569, 7, 41], [570, 7, 52], [571, 7, 75], [572, 7, 76], [573, 7, 87], [574, 7, 111], [575, 7, 57], [576, 25, 8], [577, 25, 36], [578, 25, 53], [579, 25, 58], [580, 25, 75], [581, 25, 82], [582, 25, 94], [583, 25, 57], [584, 75, 36], [585, 75, 54], [586, 75, 61], [587, 75, 57], [588, 40, 27], [589, 40, 36], [590, 40, 85], [591, 40, 106], [592, 40, 48], [593, 40, 111], [594, 40, 57], [595, 22, 26], [596, 22, 36], [597, 22, 65], [598, 22, 112], [599, 22, 57]]\n",
      "79\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print(test_dataset.dataset._class_corr)\n",
    "print(max([i[1] for i in test_dataset.dataset._class_corr]))\n",
    "print(min([i[1] for i in test_dataset.dataset._class_corr]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "600"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_dataset.dataset._class_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'id': '000',\n",
       "  'object': 'airplane',\n",
       "  'object_cat': 5,\n",
       "  'object_index': 4,\n",
       "  'verb': 'board',\n",
       "  'verb_id': 4},\n",
       " {'id': '001',\n",
       "  'object': 'airplane',\n",
       "  'object_cat': 5,\n",
       "  'object_index': 4,\n",
       "  'verb': 'direct',\n",
       "  'verb_id': 17},\n",
       " {'id': '002',\n",
       "  'object': 'airplane',\n",
       "  'object_cat': 5,\n",
       "  'object_index': 4,\n",
       "  'verb': 'exit',\n",
       "  'verb_id': 25},\n",
       " {'id': '003',\n",
       "  'object': 'airplane',\n",
       "  'object_cat': 5,\n",
       "  'object_index': 4,\n",
       "  'verb': 'fly',\n",
       "  'verb_id': 30},\n",
       " {'id': '004',\n",
       "  'object': 'airplane',\n",
       "  'object_cat': 5,\n",
       "  'object_index': 4,\n",
       "  'verb': 'inspect',\n",
       "  'verb_id': 41},\n",
       " {'id': '005',\n",
       "  'object': 'airplane',\n",
       "  'object_cat': 5,\n",
       "  'object_index': 4,\n",
       "  'verb': 'load',\n",
       "  'verb_id': 52},\n",
       " {'id': '006',\n",
       "  'object': 'airplane',\n",
       "  'object_cat': 5,\n",
       "  'object_index': 4,\n",
       "  'verb': 'ride',\n",
       "  'verb_id': 76},\n",
       " {'id': '007',\n",
       "  'object': 'airplane',\n",
       "  'object_cat': 5,\n",
       "  'object_index': 4,\n",
       "  'verb': 'sit_on',\n",
       "  'verb_id': 87},\n",
       " {'id': '008',\n",
       "  'object': 'airplane',\n",
       "  'object_cat': 5,\n",
       "  'object_index': 4,\n",
       "  'verb': 'wash',\n",
       "  'verb_id': 111},\n",
       " {'id': '009',\n",
       "  'object': 'airplane',\n",
       "  'object_cat': 5,\n",
       "  'object_index': 4,\n",
       "  'verb': 'no_interaction',\n",
       "  'verb_id': 57},\n",
       " {'id': '010',\n",
       "  'object': 'bicycle',\n",
       "  'object_cat': 2,\n",
       "  'object_index': 1,\n",
       "  'verb': 'carry',\n",
       "  'verb_id': 8},\n",
       " {'id': '011',\n",
       "  'object': 'bicycle',\n",
       "  'object_cat': 2,\n",
       "  'object_index': 1,\n",
       "  'verb': 'hold',\n",
       "  'verb_id': 36},\n",
       " {'id': '012',\n",
       "  'object': 'bicycle',\n",
       "  'object_cat': 2,\n",
       "  'object_index': 1,\n",
       "  'verb': 'inspect',\n",
       "  'verb_id': 41},\n",
       " {'id': '013',\n",
       "  'object': 'bicycle',\n",
       "  'object_cat': 2,\n",
       "  'object_index': 1,\n",
       "  'verb': 'jump',\n",
       "  'verb_id': 43},\n",
       " {'id': '014',\n",
       "  'object': 'bicycle',\n",
       "  'object_cat': 2,\n",
       "  'object_index': 1,\n",
       "  'verb': 'hop_on',\n",
       "  'verb_id': 37},\n",
       " {'id': '015',\n",
       "  'object': 'bicycle',\n",
       "  'object_cat': 2,\n",
       "  'object_index': 1,\n",
       "  'verb': 'park',\n",
       "  'verb_id': 62},\n",
       " {'id': '016',\n",
       "  'object': 'bicycle',\n",
       "  'object_cat': 2,\n",
       "  'object_index': 1,\n",
       "  'verb': 'push',\n",
       "  'verb_id': 71},\n",
       " {'id': '017',\n",
       "  'object': 'bicycle',\n",
       "  'object_cat': 2,\n",
       "  'object_index': 1,\n",
       "  'verb': 'repair',\n",
       "  'verb_id': 75},\n",
       " {'id': '018',\n",
       "  'object': 'bicycle',\n",
       "  'object_cat': 2,\n",
       "  'object_index': 1,\n",
       "  'verb': 'ride',\n",
       "  'verb_id': 76},\n",
       " {'id': '019',\n",
       "  'object': 'bicycle',\n",
       "  'object_cat': 2,\n",
       "  'object_index': 1,\n",
       "  'verb': 'sit_on',\n",
       "  'verb_id': 87},\n",
       " {'id': '020',\n",
       "  'object': 'bicycle',\n",
       "  'object_cat': 2,\n",
       "  'object_index': 1,\n",
       "  'verb': 'straddle',\n",
       "  'verb_id': 98},\n",
       " {'id': '021',\n",
       "  'object': 'bicycle',\n",
       "  'object_cat': 2,\n",
       "  'object_index': 1,\n",
       "  'verb': 'walk',\n",
       "  'verb_id': 110},\n",
       " {'id': '022',\n",
       "  'object': 'bicycle',\n",
       "  'object_cat': 2,\n",
       "  'object_index': 1,\n",
       "  'verb': 'wash',\n",
       "  'verb_id': 111},\n",
       " {'id': '023',\n",
       "  'object': 'bicycle',\n",
       "  'object_cat': 2,\n",
       "  'object_index': 1,\n",
       "  'verb': 'no_interaction',\n",
       "  'verb_id': 57},\n",
       " {'id': '024',\n",
       "  'object': 'bird',\n",
       "  'object_cat': 16,\n",
       "  'object_index': 14,\n",
       "  'verb': 'chase',\n",
       "  'verb_id': 10},\n",
       " {'id': '025',\n",
       "  'object': 'bird',\n",
       "  'object_cat': 16,\n",
       "  'object_index': 14,\n",
       "  'verb': 'feed',\n",
       "  'verb_id': 26},\n",
       " {'id': '026',\n",
       "  'object': 'bird',\n",
       "  'object_cat': 16,\n",
       "  'object_index': 14,\n",
       "  'verb': 'hold',\n",
       "  'verb_id': 36},\n",
       " {'id': '027',\n",
       "  'object': 'bird',\n",
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       "  'object': 'surfboard',\n",
       "  'object_cat': 42,\n",
       "  'object_index': 37,\n",
       "  'verb': 'no_interaction',\n",
       "  'verb_id': 57},\n",
       " {'id': '528',\n",
       "  'object': 'teddy bear',\n",
       "  'object_cat': 88,\n",
       "  'object_index': 77,\n",
       "  'verb': 'carry',\n",
       "  'verb_id': 8},\n",
       " {'id': '529',\n",
       "  'object': 'teddy bear',\n",
       "  'object_cat': 88,\n",
       "  'object_index': 77,\n",
       "  'verb': 'hold',\n",
       "  'verb_id': 36},\n",
       " {'id': '530',\n",
       "  'object': 'teddy bear',\n",
       "  'object_cat': 88,\n",
       "  'object_index': 77,\n",
       "  'verb': 'hug',\n",
       "  'verb_id': 39},\n",
       " {'id': '531',\n",
       "  'object': 'teddy bear',\n",
       "  'object_cat': 88,\n",
       "  'object_index': 77,\n",
       "  'verb': 'kiss',\n",
       "  'verb_id': 45},\n",
       " {'id': '532',\n",
       "  'object': 'teddy bear',\n",
       "  'object_cat': 88,\n",
       "  'object_index': 77,\n",
       "  'verb': 'no_interaction',\n",
       "  'verb_id': 57},\n",
       " {'id': '533',\n",
       "  'object': 'tennis racket',\n",
       "  'object_cat': 43,\n",
       "  'object_index': 38,\n",
       "  'verb': 'carry',\n",
       "  'verb_id': 8},\n",
       " {'id': '534',\n",
       "  'object': 'tennis racket',\n",
       "  'object_cat': 43,\n",
       "  'object_index': 38,\n",
       "  'verb': 'hold',\n",
       "  'verb_id': 36},\n",
       " {'id': '535',\n",
       "  'object': 'tennis racket',\n",
       "  'object_cat': 43,\n",
       "  'object_index': 38,\n",
       "  'verb': 'inspect',\n",
       "  'verb_id': 41},\n",
       " {'id': '536',\n",
       "  'object': 'tennis racket',\n",
       "  'object_cat': 43,\n",
       "  'object_index': 38,\n",
       "  'verb': 'swing',\n",
       "  'verb_id': 99},\n",
       " {'id': '537',\n",
       "  'object': 'tennis racket',\n",
       "  'object_cat': 43,\n",
       "  'object_index': 38,\n",
       "  'verb': 'no_interaction',\n",
       "  'verb_id': 57},\n",
       " {'id': '538',\n",
       "  'object': 'tie',\n",
       "  'object_cat': 32,\n",
       "  'object_index': 27,\n",
       "  'verb': 'adjust',\n",
       "  'verb_id': 0},\n",
       " {'id': '539',\n",
       "  'object': 'tie',\n",
       "  'object_cat': 32,\n",
       "  'object_index': 27,\n",
       "  'verb': 'cut',\n",
       "  'verb_id': 15},\n",
       " {'id': '540',\n",
       "  'object': 'tie',\n",
       "  'object_cat': 32,\n",
       "  'object_index': 27,\n",
       "  'verb': 'hold',\n",
       "  'verb_id': 36},\n",
       " {'id': '541',\n",
       "  'object': 'tie',\n",
       "  'object_cat': 32,\n",
       "  'object_index': 27,\n",
       "  'verb': 'inspect',\n",
       "  'verb_id': 41},\n",
       " {'id': '542',\n",
       "  'object': 'tie',\n",
       "  'object_cat': 32,\n",
       "  'object_index': 27,\n",
       "  'verb': 'pull',\n",
       "  'verb_id': 70},\n",
       " {'id': '543',\n",
       "  'object': 'tie',\n",
       "  'object_cat': 32,\n",
       "  'object_index': 27,\n",
       "  'verb': 'tie',\n",
       "  'verb_id': 105},\n",
       " {'id': '544',\n",
       "  'object': 'tie',\n",
       "  'object_cat': 32,\n",
       "  'object_index': 27,\n",
       "  'verb': 'wear',\n",
       "  'verb_id': 114},\n",
       " {'id': '545',\n",
       "  'object': 'tie',\n",
       "  'object_cat': 32,\n",
       "  'object_index': 27,\n",
       "  'verb': 'no_interaction',\n",
       "  'verb_id': 57},\n",
       " {'id': '546',\n",
       "  'object': 'toaster',\n",
       "  'object_cat': 80,\n",
       "  'object_index': 70,\n",
       "  'verb': 'hold',\n",
       "  'verb_id': 36},\n",
       " {'id': '547',\n",
       "  'object': 'toaster',\n",
       "  'object_cat': 80,\n",
       "  'object_index': 70,\n",
       "  'verb': 'operate',\n",
       "  'verb_id': 59},\n",
       " {'id': '548',\n",
       "  'object': 'toaster',\n",
       "  'object_cat': 80,\n",
       "  'object_index': 70,\n",
       "  'verb': 'repair',\n",
       "  'verb_id': 75},\n",
       " {'id': '549',\n",
       "  'object': 'toaster',\n",
       "  'object_cat': 80,\n",
       "  'object_index': 70,\n",
       "  'verb': 'no_interaction',\n",
       "  'verb_id': 57},\n",
       " {'id': '550',\n",
       "  'object': 'toilet',\n",
       "  'object_cat': 70,\n",
       "  'object_index': 61,\n",
       "  'verb': 'clean',\n",
       "  'verb_id': 12},\n",
       " {'id': '551',\n",
       "  'object': 'toilet',\n",
       "  'object_cat': 70,\n",
       "  'object_index': 61,\n",
       "  'verb': 'flush',\n",
       "  'verb_id': 29},\n",
       " {'id': '552',\n",
       "  'object': 'toilet',\n",
       "  'object_cat': 70,\n",
       "  'object_index': 61,\n",
       "  'verb': 'open',\n",
       "  'verb_id': 58},\n",
       " {'id': '553',\n",
       "  'object': 'toilet',\n",
       "  'object_cat': 70,\n",
       "  'object_index': 61,\n",
       "  'verb': 'repair',\n",
       "  'verb_id': 75},\n",
       " {'id': '554',\n",
       "  'object': 'toilet',\n",
       "  'object_cat': 70,\n",
       "  'object_index': 61,\n",
       "  'verb': 'sit_on',\n",
       "  'verb_id': 87},\n",
       " {'id': '555',\n",
       "  'object': 'toilet',\n",
       "  'object_cat': 70,\n",
       "  'object_index': 61,\n",
       "  'verb': 'stand_on',\n",
       "  'verb_id': 93},\n",
       " {'id': '556',\n",
       "  'object': 'toilet',\n",
       "  'object_cat': 70,\n",
       "  'object_index': 61,\n",
       "  'verb': 'wash',\n",
       "  'verb_id': 111},\n",
       " {'id': '557',\n",
       "  'object': 'toilet',\n",
       "  'object_cat': 70,\n",
       "  'object_index': 61,\n",
       "  'verb': 'no_interaction',\n",
       "  'verb_id': 57},\n",
       " {'id': '558',\n",
       "  'object': 'toothbrush',\n",
       "  'object_cat': 90,\n",
       "  'object_index': 79,\n",
       "  'verb': 'brush_with',\n",
       "  'verb_id': 6},\n",
       " {'id': '559',\n",
       "  'object': 'toothbrush',\n",
       "  'object_cat': 90,\n",
       "  'object_index': 79,\n",
       "  'verb': 'hold',\n",
       "  'verb_id': 36},\n",
       " {'id': '560',\n",
       "  'object': 'toothbrush',\n",
       "  'object_cat': 90,\n",
       "  'object_index': 79,\n",
       "  'verb': 'wash',\n",
       "  'verb_id': 111},\n",
       " {'id': '561',\n",
       "  'object': 'toothbrush',\n",
       "  'object_cat': 90,\n",
       "  'object_index': 79,\n",
       "  'verb': 'no_interaction',\n",
       "  'verb_id': 57},\n",
       " {'id': '562',\n",
       "  'object': 'traffic light',\n",
       "  'object_cat': 10,\n",
       "  'object_index': 9,\n",
       "  'verb': 'install',\n",
       "  'verb_id': 42},\n",
       " {'id': '563',\n",
       "  'object': 'traffic light',\n",
       "  'object_cat': 10,\n",
       "  'object_index': 9,\n",
       "  'verb': 'repair',\n",
       "  'verb_id': 75},\n",
       " {'id': '564',\n",
       "  'object': 'traffic light',\n",
       "  'object_cat': 10,\n",
       "  'object_index': 9,\n",
       "  'verb': 'stand_under',\n",
       "  'verb_id': 94},\n",
       " {'id': '565',\n",
       "  'object': 'traffic light',\n",
       "  'object_cat': 10,\n",
       "  'object_index': 9,\n",
       "  'verb': 'stop_at',\n",
       "  'verb_id': 97},\n",
       " {'id': '566',\n",
       "  'object': 'traffic light',\n",
       "  'object_cat': 10,\n",
       "  'object_index': 9,\n",
       "  'verb': 'no_interaction',\n",
       "  'verb_id': 57},\n",
       " {'id': '567',\n",
       "  'object': 'truck',\n",
       "  'object_cat': 8,\n",
       "  'object_index': 7,\n",
       "  'verb': 'direct',\n",
       "  'verb_id': 17},\n",
       " {'id': '568',\n",
       "  'object': 'truck',\n",
       "  'object_cat': 8,\n",
       "  'object_index': 7,\n",
       "  'verb': 'drive',\n",
       "  'verb_id': 21},\n",
       " {'id': '569',\n",
       "  'object': 'truck',\n",
       "  'object_cat': 8,\n",
       "  'object_index': 7,\n",
       "  'verb': 'inspect',\n",
       "  'verb_id': 41},\n",
       " {'id': '570',\n",
       "  'object': 'truck',\n",
       "  'object_cat': 8,\n",
       "  'object_index': 7,\n",
       "  'verb': 'load',\n",
       "  'verb_id': 52},\n",
       " {'id': '571',\n",
       "  'object': 'truck',\n",
       "  'object_cat': 8,\n",
       "  'object_index': 7,\n",
       "  'verb': 'repair',\n",
       "  'verb_id': 75},\n",
       " {'id': '572',\n",
       "  'object': 'truck',\n",
       "  'object_cat': 8,\n",
       "  'object_index': 7,\n",
       "  'verb': 'ride',\n",
       "  'verb_id': 76},\n",
       " {'id': '573',\n",
       "  'object': 'truck',\n",
       "  'object_cat': 8,\n",
       "  'object_index': 7,\n",
       "  'verb': 'sit_on',\n",
       "  'verb_id': 87},\n",
       " {'id': '574',\n",
       "  'object': 'truck',\n",
       "  'object_cat': 8,\n",
       "  'object_index': 7,\n",
       "  'verb': 'wash',\n",
       "  'verb_id': 111},\n",
       " {'id': '575',\n",
       "  'object': 'truck',\n",
       "  'object_cat': 8,\n",
       "  'object_index': 7,\n",
       "  'verb': 'no_interaction',\n",
       "  'verb_id': 57},\n",
       " {'id': '576',\n",
       "  'object': 'umbrella',\n",
       "  'object_cat': 28,\n",
       "  'object_index': 25,\n",
       "  'verb': 'carry',\n",
       "  'verb_id': 8},\n",
       " {'id': '577',\n",
       "  'object': 'umbrella',\n",
       "  'object_cat': 28,\n",
       "  'object_index': 25,\n",
       "  'verb': 'hold',\n",
       "  'verb_id': 36},\n",
       " {'id': '578',\n",
       "  'object': 'umbrella',\n",
       "  'object_cat': 28,\n",
       "  'object_index': 25,\n",
       "  'verb': 'lose',\n",
       "  'verb_id': 53},\n",
       " {'id': '579',\n",
       "  'object': 'umbrella',\n",
       "  'object_cat': 28,\n",
       "  'object_index': 25,\n",
       "  'verb': 'open',\n",
       "  'verb_id': 58},\n",
       " {'id': '580',\n",
       "  'object': 'umbrella',\n",
       "  'object_cat': 28,\n",
       "  'object_index': 25,\n",
       "  'verb': 'repair',\n",
       "  'verb_id': 75},\n",
       " {'id': '581',\n",
       "  'object': 'umbrella',\n",
       "  'object_cat': 28,\n",
       "  'object_index': 25,\n",
       "  'verb': 'set',\n",
       "  'verb_id': 82},\n",
       " {'id': '582',\n",
       "  'object': 'umbrella',\n",
       "  'object_cat': 28,\n",
       "  'object_index': 25,\n",
       "  'verb': 'stand_under',\n",
       "  'verb_id': 94},\n",
       " {'id': '583',\n",
       "  'object': 'umbrella',\n",
       "  'object_cat': 28,\n",
       "  'object_index': 25,\n",
       "  'verb': 'no_interaction',\n",
       "  'verb_id': 57},\n",
       " {'id': '584',\n",
       "  'object': 'vase',\n",
       "  'object_cat': 86,\n",
       "  'object_index': 75,\n",
       "  'verb': 'hold',\n",
       "  'verb_id': 36},\n",
       " {'id': '585',\n",
       "  'object': 'vase',\n",
       "  'object_cat': 86,\n",
       "  'object_index': 75,\n",
       "  'verb': 'make',\n",
       "  'verb_id': 54},\n",
       " {'id': '586',\n",
       "  'object': 'vase',\n",
       "  'object_cat': 86,\n",
       "  'object_index': 75,\n",
       "  'verb': 'paint',\n",
       "  'verb_id': 61},\n",
       " {'id': '587',\n",
       "  'object': 'vase',\n",
       "  'object_cat': 86,\n",
       "  'object_index': 75,\n",
       "  'verb': 'no_interaction',\n",
       "  'verb_id': 57},\n",
       " {'id': '588',\n",
       "  'object': 'wine glass',\n",
       "  'object_cat': 46,\n",
       "  'object_index': 40,\n",
       "  'verb': 'fill',\n",
       "  'verb_id': 27},\n",
       " {'id': '589',\n",
       "  'object': 'wine glass',\n",
       "  'object_cat': 46,\n",
       "  'object_index': 40,\n",
       "  'verb': 'hold',\n",
       "  'verb_id': 36},\n",
       " {'id': '590',\n",
       "  'object': 'wine glass',\n",
       "  'object_cat': 46,\n",
       "  'object_index': 40,\n",
       "  'verb': 'sip',\n",
       "  'verb_id': 85},\n",
       " {'id': '591',\n",
       "  'object': 'wine glass',\n",
       "  'object_cat': 46,\n",
       "  'object_index': 40,\n",
       "  'verb': 'toast',\n",
       "  'verb_id': 106},\n",
       " {'id': '592',\n",
       "  'object': 'wine glass',\n",
       "  'object_cat': 46,\n",
       "  'object_index': 40,\n",
       "  'verb': 'lick',\n",
       "  'verb_id': 48},\n",
       " {'id': '593',\n",
       "  'object': 'wine glass',\n",
       "  'object_cat': 46,\n",
       "  'object_index': 40,\n",
       "  'verb': 'wash',\n",
       "  'verb_id': 111},\n",
       " {'id': '594',\n",
       "  'object': 'wine glass',\n",
       "  'object_cat': 46,\n",
       "  'object_index': 40,\n",
       "  'verb': 'no_interaction',\n",
       "  'verb_id': 57},\n",
       " {'id': '595',\n",
       "  'object': 'zebra',\n",
       "  'object_cat': 24,\n",
       "  'object_index': 22,\n",
       "  'verb': 'feed',\n",
       "  'verb_id': 26},\n",
       " {'id': '596',\n",
       "  'object': 'zebra',\n",
       "  'object_cat': 24,\n",
       "  'object_index': 22,\n",
       "  'verb': 'hold',\n",
       "  'verb_id': 36},\n",
       " {'id': '597',\n",
       "  'object': 'zebra',\n",
       "  'object_cat': 24,\n",
       "  'object_index': 22,\n",
       "  'verb': 'pet',\n",
       "  'verb_id': 65},\n",
       " {'id': '598',\n",
       "  'object': 'zebra',\n",
       "  'object_cat': 24,\n",
       "  'object_index': 22,\n",
       "  'verb': 'watch',\n",
       "  'verb_id': 112},\n",
       " {'id': '599',\n",
       "  'object': 'zebra',\n",
       "  'object_cat': 24,\n",
       "  'object_index': 22,\n",
       "  'verb': 'no_interaction',\n",
       "  'verb_id': 57}]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i, x in enumerate(hoi_list):\n",
    "    num = test_dataset.dataset._class_corr[i]\n",
    "    \n",
    "    x['id']=\"{}\".format(num[0]).zfill(3)\n",
    "    x['object_index'] = num[1]\n",
    "    x['object']=OBJECTS[num[1]]\n",
    "    x['object_cat']=cov[num[1]]\n",
    "    x['verb'] = hico_verb_name_dict[num[2]]\n",
    "    x['verb_id'] = num[2]\n",
    "    \n",
    "hoi_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# anno_file = 'hicodet/hoi_list_new.json'\n",
    "# with open(anno_file, 'w') as f:\n",
    "#     json.dump(hoi_list, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min([ max(i) for i in file_name_to_obj_cat.values()])\n",
    "max([ max(i) for i in file_name_to_obj_cat.values()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'6.62'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"{:.3}\".format((37.37/35.05-1)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'8.86'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"{:.3}\".format((38.81/35.65-1)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.77777777777777"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(93*1+89*1+85*3+84*2+95*3+94*2+91*2+89*2+98*2)/(1+1+3+2+3+2+2+2+2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05891238670694851\n"
     ]
    }
   ],
   "source": [
    "x=70.1\n",
    "y=66.2\n",
    "\n",
    "print((x-y)/y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1172077997.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"/tmp/ipykernel_187891/1172077997.py\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    68.3\t70.5\u001b[0m\n\u001b[0m        \t^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "68.3\t70.5\n",
    "70.1\t72.3\t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9000000000000057"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "70.5-68.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.7170208764635\n"
     ]
    }
   ],
   "source": [
    "str1 = \"\"\"infer_time: 0.1021425724029541\n",
    "infer_time: 0.08861088752746582\n",
    "infer_time: 0.09287834167480469\n",
    "infer_time: 0.07823586463928223\n",
    "infer_time: 0.09989380836486816\n",
    "infer_time: 0.0781240463256836\n",
    "infer_time: 0.0793619155883789\n",
    "infer_time: 0.0915365219116211\n",
    "infer_time: 0.07943224906921387\n",
    "infer_time: 0.08656764030456543\n",
    "infer_time: 0.08954548835754395\n",
    "infer_time: 0.07409954071044922\n",
    "infer_time: 0.08506178855895996\n",
    "infer_time: 0.09167194366455078\n",
    "infer_time: 0.07763290405273438\n",
    "infer_time: 0.08026671409606934\n",
    "infer_time: 0.08537673950195312\n",
    "infer_time: 0.0791165828704834\n",
    "infer_time: 0.09714651107788086\n",
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    "infer_time: 0.09022808074951172\n",
    "infer_time: 0.10416650772094727\n",
    "infer_time: 0.0891256332397461\n",
    "infer_time: 0.08480501174926758\n",
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    "infer_time: 0.08219027519226074\n",
    "infer_time: 0.10421490669250488\n",
    "infer_time: 0.0800321102142334\n",
    "infer_time: 0.0887753963470459\n",
    "infer_time: 0.08664417266845703\n",
    "infer_time: 0.07906293869018555\"\"\"\n",
    "\n",
    "times = str1.split('\\n')\n",
    "t = 0\n",
    "for time_ in times:\n",
    "    real_time = time_.split(' ')\n",
    "    t += float(real_time[-1])\n",
    "\n",
    "x = t / len(times)\n",
    "print(1 / x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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